SOTAVerified

Gaussian Processes

Gaussian Processes is a powerful framework for several machine learning tasks such as regression, classification and inference. Given a finite set of input output training data that is generated out of a fixed (but possibly unknown) function, the framework models the unknown function as a stochastic process such that the training outputs are a finite number of jointly Gaussian random variables, whose properties can then be used to infer the statistics (the mean and variance) of the function at test values of input.

Source: Sequential Randomized Matrix Factorization for Gaussian Processes: Efficient Predictions and Hyper-parameter Optimization

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 776–800 of 1963 papers

TitleStatusHype
Gaussian Process Molecule Property Prediction with FlowMO—0
Bayesian Warped Gaussian Processes—0
Gaussian Process Neurons—0
Efficient Inference of Gaussian Process Modulated Renewal Processes with Application to Medical Event Data—0
Gaussian Process on the Product of Directional Manifolds—0
A Perspective on Gaussian Processes for Earth Observation—0
Bayesian Variational Optimization for Combinatorial Spaces—0
Gaussian Process Position-Dependent Feedforward: With Application to a Wire Bonder—0
Convolutional Normalizing Flows for Deep Gaussian Processes—0
Gaussian Process Pseudo-Likelihood Models for Sequence Labeling—0
Cooperative Learning with Gaussian Processes for Euler-Lagrange Systems Tracking Control under Switching Topologies—0
Gaussian Process Regression constrained by Boundary Value Problems—0
Gaussian Process Regression for Inverse Problems in Linear PDEs—0
Gaussian Process Regression for Maximum Entropy Distribution—0
Correlated Product of Experts for Sparse Gaussian Process Regression—0
A computationally lightweight safe learning algorithm—0
Gaussian Process Subset Scanning for Anomalous Pattern Detection in Non-iid Data—0
Gaussian Process Surrogate Models for Neural Networks—0
Gaussian process surrogate model to approximate power grid simulators -- An application to the certification of a congestion management controller—0
GPTreeO: An R package for continual regression with dividing local Gaussian processes—0
Gaussian Process Volatility Model—0
Gauss-Legendre Features for Gaussian Process Regression—0
Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference—0
Generalization Errors and Learning Curves for Regression with Multi-task Gaussian Processes—0
Efficient Global Optimization using Deep Gaussian Processes—0
Show:102550
← PrevPage 32 of 79Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ICKy, periodicRoot mean square error (RMSE)0.03—Unverified